An Effective System of Intrusion Detection on Deep Neural Network by Hybrid Optimization in Cyber Security
Thupakula Bhaskar1, Tryambak Hiwarkar2, K. Ramanjaneyulu3
1A. Thupakula Bhaskar *, Research-Scholar, Department of Computer Science and Engineering, Sri Satya Sai University of Technology & Medical Sciences, Bhopal, M. P. India.
2B. Dr.Tryambak Hiwarkar, Professor, Department of Computer Science and Engineering, Sri Satya Sai University of Technology & Medical Sciences, Bhopal, M. P. India.
3C. Dr.K. Ramanjaneyulu, Professor, Department of ECE, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, A. P. India.
Manuscript received on October 01, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 1320-1327 | Volume-9 Issue-1, October 2019. | Retrieval Number: A1155109119/2019©BEIESP | DOI: 10.35940/ijeat.A1155.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In present trends organizations are very much interested to protect data and prevent malware attack by using well flourished and excellent tools. Many algorithms are used for the intrusion detection system (IDS) and it has pros and cons. Here we proposed a novel method of intrusion detection using hybrid optimization techniques such as Gravity search algorithm with gray wolf optimization (GSGW). In this method the gray wolf technique has a leader for the continuous monitoring of the attacker and has a low false alarm rate and a high detection rate. The performance evaluation is done by the feature selection in NSL-KDD dataset. In the proposed method the experimental result reveals less false alarm rate, better accuracy and high Detection when compared to previous analysis.
Keywords: FAR (False Alarm Rate), DR (Detection Rate), IDS, Gravity search Gray wolf optimization (GSGW).